Applying New ML Techniques to Uncover Duplicate and Derivative Data

Learn how to discover, identify, and minimize duplicate and similar data.

 

With data growing exponentially, data sources spread across many areas, including data and multiple clouds. This leads to difficulty in proactively reducing risk and protecting critical and sensitive data. One of the largest sources of risk comes from duplicate and redundant sensitive data migrating across multiple data sources and stores. Blindspots into your derivative data can create unnecessary data exposure risks; stall cloud migration initiatives, data minimization initiatives, and M&A processes; and present an additional layer of compliance challenges across the board. Join BigID and (ISC)2 for a discussion about these risks and how to discover, identify, and minimize duplicate and similar data. Areas covered will include:

  • How to identify and tag duplicate, similar, and redundant data
  • Map Data migration and identify original data sources
  • Best practices for minimizing critical data across data sources and removing duplicate data
  • How to apply next-gen ML techniques to reduce risk and increase confidence in your data
  • Build a data driven risk profile of your data sources

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